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ESTRO 35 2016 S971

________________________________________________________________________________

preterm stop of the therapy thus jeopardizing the intended

treatment outcome. Despite numerous research attempts

there is still no robust feature established in clinical routine

to predict radiotherapy-induced toxicity prior to therapy

start.

Material and Methods:

The study cohort comprised 40

patients who underwent neoadjuvant radiochemotherapy (N-

RCT) for rectal cancer (28x1.8 Gy, 5 times weekly,

concomitant with two cycles 5-FU-based chemotherapy).

From each of those patients dermal fibroblasts were cultured

from skin specimen gained outside of the radiotherapy

planning target volume at occasion of surgery conducted

about six weeks upon N-RCT completion. Acute radiotoxicity

was thoroughly monitored throughout the N-RCT series and

documented according to CTC classification. Maximal acute

toxicity (MAT) was defined by the highest CTC grade of the

four items “cystitis”, “proctitis”, “enteritis”, and

“dermatitis”. MAT was grouped into grades 0/1 (n=16), 2

(n=16), and 3/4 (n=8). N-RCT was simulated in the cultured

fibroblasts for five consecutive days (1.8 Gy each at d1-d5

with addiation of 5-FU at a concentration reflecting clinical

steady-state levels) followed by a 7-day wash-out period.

Gene expression of nine candidate genes (

CAT

,

CDKN1A

,

CTGF

,

SMAD2/3/4/7

,

TGFB1

,

TGFBR1

) supposed to mediate

early radiation-induced toxicity was ascertained by

quantitative real time PCR. Samples for these RNA analyses

were harvested at d2 and d5 (each 4 hours upon application

of the radiation fraction) as well as at day 12 upon the wash-

out period.

GAPDH

and

HPRT1

transcript levels served as

reference.

Results:

MAT was related to radiation-induced expression

changes of four of the considered genes in fibroblasts. The

strongest impact was obtained for

SMAD7

and

CAT

at d5. The

higher the MAT score, the lower the induction of

SMAD7

and

CAT

by radiation was (p=0.001 and 0.003). However, upon

the wash-out period at d12 no statistical differences in

dependence on the MAT score were seen anymore for these

two genes. In contrast, a high MAT score was linked to low

radiation-induced induction of

CTGF

(p=0.005) and to a faster

decrease of the massively induced

CDKN1A

(p=0.03) at d12.

At d2, a trend (p=0.06) for

CAT

in relation to MAT in the

same direction as at d5 was noticed with no correlation of

any of the other genes at this early time point.

Conclusion:

Radiation-induced expression changes in patient-

matched fibroblasts may serve as biomarkers to predict

clinical radiotoxicity. Induction of

SMAD7

and

CAT

may

mitigate TGFbeta signalling and reactive oxygen species load

thus saving normal tissue during radiotherapy. A protective

role might also be attributed to sustained elevation of

CDKN1A

. The link between post-radiation induction of

CTGF

in fibroblasts and low MAT remains to be clarified.

Understanding the mechanistic basis of these findings might

pave the way for better protection of irradiated normal

tissue.

EP-2058

A novel multi-SNP model predictive of erectile dysfunction

following radiotherapy in prostate cancer

J.H. Oh

1

Memorial Sloan Kettering Cancer Center, Department of

Medical Physics, New York, USA

1

, S. Kerns

2

, H. Ostrer

3

, B. Rosenstein

4

, J.O. Deasy

1

2

University of Rochester Medical Center, Department of

Radiation Oncology, Rochester, USA

3

Albert Einstein College of Medicine, Departments of

Pathology and Genetics, New York, USA

4

Mount Sinai School of Medicine, Department of Radiation

Oncology, New York, USA

Purpose or Objective:

Erectile dysfunction (ED) is one of the

most common complications encountered after radiotherapy

in prostate cancer patients. The goal of this study was to

investigate whether single nucleotide polymorphisms (SNPs)

are associated with late ED in men treated with radiotherapy

for prostate cancer. To this end, we designed a novel

machine learning-based multi-SNP model using a genome-

wide association study (GWAS) dataset.

Material and Methods:

We analyzed 236 evaluable patients

with at least one year of follow-up for the development of

ED. The severity of ED was assessed using either the patient-

administered Sexual Health Inventory for Men (SHIM) or the

clinician-assigned Mount Sinai Erectile Function (MSEF)

scoring schema. There were 133 patients with Grade 2 or

more ED. For our analysis, the cohort was split into two

groups (cases/controls: MSEF 0,1 / 2,3; cases/controls: SHIM

≤7 / ≥16). Genome-wide SNP data were available from

Affymetrix Genome-Wide Human SNP Array 6.0. After a

quality test including SNP missing rate>5%, minor allele

frequency (MAF)<5%, and Hardy-Weinberg equilibrium

(p<10E-5), 613,496 SNPs remained.

For the validation purpose of our proposed model, the

dataset was split into a training dataset (2/3 of samples) and

a validation dataset (1/3 of samples). Our model building

process was performed using the bootstrapped data from the

training dataset. Our idea is to convert the binary outcomes

into preconditioned continuous outcomes based on normal

tissue complication probability (NTCP) using principal

component analysis (PCA) and logistic regression. The

preconditioned outcomes were used in the model building

process using random forest regression. Then, the model was

tested using the validation dataset. The final predicted

outcomes were compared with the original binary outcomes

to estimate the predictive performance. We iterated this

process 100 times and the performance was averaged. We

compared the performance of our proposed method

(preconditioning random forest regression: PRFR) with other

methods including preconditioning lasso (PL), lasso logistic

regression (LLR), and random forest classification (RFC).

Results:

Univariate analysis was performed using the training

dataset. With a threshold of p=0.001, 367 SNPs remained.

These SNPs were fed into our model. As shown in Figure (A),

the averaged performance with the validation dataset was

AUC=0.62, which is better than other methods: RFC (0.57),

PL (0.58), and LLR (0.57). The 79 patients in the validation

dataset were binned into 6 groups according to the predicted

risk of ED. Figure (B) shows the comparison of the model-

predicted incidence of ED with observed incidence.

Conclusion:

Our machine learning-based multi-SNP model

showed the potential to better predict the radiation-induced

late ED. However, we need to validate our model using other

datasets.

EP-2059

Changes in hypoxia in serial F-MISO/PET-CT during

chemoradiation in HNSCC

H. Kerti

1

University Hospital Freiburg, Department of Radiation

Oncology, Freiburg, Germany

1

, A. Bunea

1

, L. Majerus

1

, M. Mix

2

, C. Stoykow

2

, N.

Wiedenmann

1

, P.T. Meyer

2

, A.L. Grosu

1

2

University Hospital Freiburg, Department of Nuclear

Medicine, Freiburg, Germany

Purpose or Objective:

Tumor hypoxia, a common feature of

locally advanced head and neck cancer (HNSCC), is

associated with higher malignancy and increased

radioresistance. The decrease of tumor hypoxia during